Space-time adaptive processing
Radar signal processing benefits from STAP in areas where interference is a problem (i.e. ground clutter, jamming, etc.).
Through careful application of STAP, it is possible to achieve order-of-magnitude sensitivity improvements in target detection.
STAP involves a two-dimensional filtering technique using a phased-array antenna with multiple spatial channels.
Applying the statistics of the interference environment, an adaptive STAP weight vector is formed.
The theory of STAP was first published by Lawrence E. Brennan and Irving S. Reed in the early 1970s.
[2] For ground-based radar, cluttered returns tend to be at DC, making them easily discriminated by Moving Target Indication (MTI).
[2] In this case, 1D filtering is not sufficient, since clutter can overlap the desired target's Doppler from multiple directions.
[2] The resulting interference is typically called a "clutter ridge," since it forms a line in the angle-Doppler domain.
[2] This means that we are filtering over multiple dimensions, and multi-dimensional signal processing techniques must be employed.
is the number of pulse-repetition interval (PRI) taps (our time degrees of freedom), to maximize the signal-to-interference and noise ratio (SINR).
[2] Thus, the goal is to suppress noise, clutter, jammers, etc., while keeping the desired radar return.
[2] Unfortunately, in practice, this is oversimplified, as the interference to be overcome by steering the nulls shown is not deterministic, but statistical in nature.
Note that even in this idealized example, in general, we must steer over the 2-D angle-Doppler plane at discrete points to detect potential targets (moving the location of the 2-D sinc main lobe shown in the figure), and do so for each of the range bins in our system.
Then, a 1-D FIR filter with PRI length delay elements is used for each steered antenna channel.
The main difficulty of STAP is solving for and inverting the typically unknown interference covariance matrix,
[1] Other difficulties arise when the interference covariance matrix is ill-conditioned, making the inversion numerically unstable.
[5] In general, this adaptive filtering must be performed for each of the unambiguous range bins in the system, for each target of interest (angle-Doppler coordinates), making for a massive computational burden.
[2] The optimum solution is using all degrees of freedom by processing the adaptive filter on the antenna elements.
[1] The main problem with direct methods is the great computational complexity associated with the estimation and inversion of matrices formed from many degrees of freedom (large number of elements and or pulses).
[4] As a result, for high dimensional systems, this may require an unachievable number of unambiguous range cells.
Post Doppler methods may also be used on the full antenna element input as well to reduce the data in this dimension only.
A popular example is displaced phase center antenna (DPCA), which is a form of data-independent STAP in the beamspace, pre-Doppler.
[2] Since the target response is already steered to a specified angle-Doppler location, the dimensionality can be reduced by pre-processing multiple Doppler bins and angles surrounding this point.
[1] There are a number of techniques to compare the performance of reduced-rank methods and estimated direct methods to clairvoyant STAP (direct with perfect knowledge of interference covariance matrix and target steering vector), mostly based around SINR loss.
[1] There are also model based methods that attempt to force or exploit the structure of the covariance interference matrix.
[2] This modeling has an added benefit of decorrelating interference subspace leakage (ISL), and is resistant to internal clutter motion (ICM).
Since fewer samples are used in the training data, the matrix often requires stabilization in the form of diagonal loading.
Frequency-selective channel compensation can be used to extend traditional equalization techniques for SISO systems using STAP.
[9] New algorithms and formulations are required that depart from the standard technique due to the large rank of the jammer-clutter subspace created by MIMO radar virtual arrays,[9] which typically involves exploiting the block diagonal structure of the MIMO interference covariance matrix to break the large matrix inversion problem into smaller ones.
degrees of freedom, allowing for much greater adaptive spatial resolution for clutter mitigation.
